Here are some of the RAW files I use to test the changes I make to denoising modules (including the one I used as an exemple in the beginning of this conversation): https://drive.google.com/open?id=11LxZWpZbS66m7vFdcoIHNTiG20JnwlJT The reference-jpg folder contains the JPGs produced by the camera for these raws (except for 2 of the RAWs for which I don't have the reference JPG). I also use several other RAW files to test, but unfortunately I cannot upload them as either they were not made by me, either they are photos of people.
These are really noisy pictures, as I would like to be able to easily process such pictures in darktable and to reach levels of quality similar or better than the cameras. Hope it will help. If you have noisy photos you would like to share too, I'd like to have them as my database of noisy pictures is a little biased (majority of photos in my little "noisy database" are from my own cameras Lumix FZ1000 and Fuji XT20 and I'd like to have more photos from other cameras) Thanks! rawfiner 2018-06-13 23:31 GMT+02:00 rawfiner <rawfi...@gmail.com>: > > > Le mercredi 13 juin 2018, Aurélien Pierre <rese...@aurelienpierre.com> a > écrit : > >> >> >>> On Thu, Jun 14, 2018 at 12:23 AM, Aurélien Pierre >>> <rese...@aurelienpierre.com> wrote: >>> > Hi, >>> > >>> > The problem of a 2-passes denoising method involving 2 differents >>> > algorithms, the later applied where the former failed, could be the >>> grain >>> > structure (the shape of the noise) would be different along the >>> picture, >>> > thus very unpleasing. >> >> >> I agree that the grain structure could be different. Indeed, the grain >> could be different, but my feeling (that may be wrong) is that it would be >> still better than just no further processing, that leaves some pixels >> unprocessed (they could form grain structures far from uniform if we are >> not lucky). >> If you think it is only due to a change of algorithm, I guess we could >> apply non local means again on pixels where a first pass failed, but with >> different parameters to be quite confident that the second pass will work. >> >> That sounds better to me… but practice will have the last word. >> > > Ok :-) > >> >> >>> > >>> > I thought maybe we could instead create some sort of total variation >>> > threshold on other denoising modules : >>> > >>> > compute the total variation of each channel of each pixel as the >>> divergence >>> > divided by the L1 norm of the gradient - we then obtain a "heatmap" of >>> the >>> > gradients over the picture (contours and noise) >>> > let the user define a total variation threshold and form a mask where >>> the >>> > weights above the threshold are the total variation and the weights >>> below >>> > the threshold are zeros (sort of a highpass filter actually) >>> > apply the bilateral filter according to this mask. >>> > >>> > This way, if the user wants to stack several denoising modules, he >>> could >>> > protect the already-cleaned areas from further denoising. >>> > >>> > What do you think ? >> >> >> That sounds interesting. >> This would maybe allow to keep some small variations/details that are not >> due to noise or not disturbing, while denoising the other parts. >> Also, it may be computationally interesting (depends on the complexity of >> the total variation computation, I don't know it), as it could reduce the >> number of pixels to process. >> I guess the user could use something like that also the other way?: to >> protect high detailed zones and apply the denoising on quite smoothed zones >> only, in order to be able to use stronger denoising on zones that are >> supposed to be background blur. >> >> >> The noise is high frequency, so the TV (total variation) threshold will >> have to be high pass only. The hypothesis behind the TV thresholding is >> noisy pixels should have abnormally higher gradients than true details, so >> you isolate them this way. Selecting noise in low frequencies areas would >> require in addition something like a guided filter, which I believe is what >> is used in the dehaze module. The complexity of the TV computation depends >> on the order of accuracy you expect. >> >> A classic approximation of the gradient is using a convolution product >> with Sobel or Prewitt operators (3×3 arrays, very efficient, fairly >> accurate for edges, probably less accurate for punctual noise). I have >> developped myself optimized methods using 2, 4, and 8 neighbouring pixels >> that give higher order accuracy, given the sparsity of the data, at the >> expense of computing cost : https://github.com/aurelienpie >> rre/Image-Cases-Studies/blob/947fd8d5c2e4c3384c80c1045d86f8c >> f89ddcc7e/lib/deconvolution.pyx#L342 (ignore the variable ut in the >> code, only u is relevant for us here). >> >> Great, thanks for the explanations. > Looking at the code of the 8 neighbouring pixels, I wonder if we would > make sense to compute something like that on raw data considering only > neighbouring pixels of the same color? > > Also, when talking about the mask formed from the heat map, do you mean > that the "heat" would give for each pixel a weight to use between input and > output? (i.e. a mask that is not only ones and zeros, but that controls how > much input and output are used for each pixel) > If so, I think it is a good idea to explore! > > rawfiner > >> >> >> >>> > >>> > Aurélien. >>> > >>> > >>> > Le 13/06/2018 à 03:16, rawfiner a écrit : >>> > >>> > Hi, >>> > >>> > I don't have the feeling that increasing K is the best way to improve >>> noise >>> > reduction anymore. >>> > I will upload the raw next week (if I don't forget to), as I am not at >>> home >>> > this week. >>> > My feeling is that doing non local means on raw data gives much bigger >>> > improvement than that. >>> > I still have to work on it yet. >>> > I am currently testing some raw downsizing ideas to allow a fast >>> execution >>> > of the algorithm. >>> > >>> > Apart of that, I also think that to improve noise reduction such as the >>> > denoise profile in nlm mode and the denoise non local means, we could >>> do a 2 >>> > passes algorithm, with non local means applied first, and then a >>> bilateral >>> > filter (or median filter or something else) applied only on pixels >>> where non >>> > local means failed to find suitable patches (i.e. pixels where the sum >>> of >>> > weights was close to 0). >>> > The user would have a slider to adjust this setting. >>> > I think that it would make easier to have a "uniform" output (i.e. an >>> output >>> > where noise has been reduced quite uniformly) >>> > I have not tested this idea yet. >>> > >>> > Cheers, >>> > rawfiner >>> > >>> > Le lundi 11 juin 2018, johannes hanika <hana...@gmail.com> a écrit : >>> >> >>> >> hi, >>> >> >>> >> i was playing with noise reduction presets again and tried the large >>> >> neighbourhood search window. on my shots i could very rarely spot a >>> >> difference at all increasing K above 7, and even less so going above >>> >> 10. the image you posted earlier did show quite a substantial >>> >> improvement however. i was wondering whether you'd be able to share >>> >> the image so i can evaluate on it? maybe i just haven't found the >>> >> right test image yet, or maybe it's camera dependent? >>> >> >>> >> (and yes, automatic and adaptive would be better but if we can ship a >>> >> simple slider that can improve matters, maybe we should) >>> >> >>> >> cheers, >>> >> jo >>> >> >>> >> >>> >> >>> >> On Mon, Jan 29, 2018 at 2:05 AM, rawfiner <rawfi...@gmail.com> wrote: >>> >> > Hi >>> >> > >>> >> > Yes, the patch size is set to 1 from the GUI, so it is not a >>> bilateral >>> >> > filter, and I guess it corresponds to a patch window size of 3x3 in >>> the >>> >> > code. >>> >> > The runtime difference is near the expected quadratic slowdown: >>> >> > 1,460 secs (8,379 CPU) for 7 and 12,794 secs (85,972 CPU) for 25, >>> which >>> >> > means about 10.26x slowdown >>> >> > >>> >> > If you want to make your mind on it, I have pushed a branch here >>> that >>> >> > integrates the K parameter in the GUI: >>> >> > https://github.com/rawfiner/darktable.git >>> >> > The branch is denoise-profile-GUI-K >>> >> > >>> >> > I think that it may be worth to see if an automated approach for the >>> >> > choice >>> >> > of K may work, in order not to integrate the parameter in the GUI. >>> >> > I may try to implement the approach of Kervann and Boulanger (the >>> >> > reference >>> >> > from the darktable blog post) to see how it performs. >>> >> > >>> >> > cheers, >>> >> > rawfiner >>> >> > >>> >> > >>> >> > 2018-01-27 13:50 GMT+01:00 johannes hanika <hana...@gmail.com>: >>> >> >> >>> >> >> heya, >>> >> >> >>> >> >> thanks for the reference! interesting interpretation how the >>> blotches >>> >> >> form. not sure i'm entirely convinced by that argument. >>> >> >> your image does look convincing though. let me get this right.. you >>> >> >> ran with radius 1 which means patch window size 3x3? not 1x1 which >>> >> >> would be a bilateral filter effectively? >>> >> >> >>> >> >> also what was the run time difference? is it near the expected >>> >> >> quadratic slowdown from 7 (i.e. 15x15) to 25 (51x51) so about >>> 11.56x >>> >> >> slower with the large window size? (test with darktable -d perf) >>> >> >> >>> >> >> since nlmeans isn't the fastest thing, even with this coalesced >>> way of >>> >> >> implementing it, we should certainly keep an eye on this. >>> >> >> >>> >> >> that being said if we can often times get much better results we >>> >> >> should totally expose this in the gui, maybe with a big warning >>> that >>> >> >> it really severely impacts speed. >>> >> >> >>> >> >> cheers, >>> >> >> jo >>> >> >> >>> >> >> On Sat, Jan 27, 2018 at 7:34 AM, rawfiner <rawfi...@gmail.com> >>> wrote: >>> >> >> > Thank you for your answer >>> >> >> > I perfectly agree with the fact that the GUI should not become >>> >> >> > overcomplicated. >>> >> >> > >>> >> >> > As far as I understand, the pixels within a small zone may suffer >>> >> >> > from >>> >> >> > correlated noise, and there is a risk of noise to noise matching. >>> >> >> > That's why this paper suggest not to take pixels that are too >>> close >>> >> >> > to >>> >> >> > the >>> >> >> > zone we are correcting, but to take them a little farther (see >>> the >>> >> >> > caption >>> >> >> > of Figure 2 for a quick explaination): >>> >> >> > >>> >> >> > >>> >> >> > >>> >> >> > https://pdfs.semanticscholar.org/c458/71830cf535ebe6c2b7656f >>> 6a205033761fc0.pdf >>> >> >> > (in case you ask, unfortunately there is a patent associated with >>> >> >> > this >>> >> >> > approach, so we cannot implement it) >>> >> >> > >>> >> >> > Increasing the neighborhood parameter results in having >>> >> >> > proportionally >>> >> >> > less >>> >> >> > problem of correlation between surrounding pixels, and decreases >>> the >>> >> >> > size of >>> >> >> > the visible spots. >>> >> >> > See for example the two attached pictures: one with size 1, >>> force 1, >>> >> >> > and >>> >> >> > K 7 >>> >> >> > and the other with size 1, force 1, and K 25. >>> >> >> > >>> >> >> > I think that the best would probably be to adapt K >>> automatically, in >>> >> >> > order >>> >> >> > not to affect the GUI, and as we may have different levels of >>> noise >>> >> >> > in >>> >> >> > different parts of an image. >>> >> >> > In this post >>> >> >> > >>> >> >> > (https://www.darktable.org/2012/12/profiling-sensor-and-phot >>> on-noise/), >>> >> >> > this >>> >> >> > paper is cited: >>> >> >> > >>> >> >> > [4] charles kervrann and jerome boulanger: optimal spatial >>> adaptation >>> >> >> > for >>> >> >> > patch-based image denoising. ieee trans. image process. vol. 15, >>> no. >>> >> >> > 10, >>> >> >> > 2006 >>> >> >> > >>> >> >> > As far as I understand, it gives a way to choose an adaptated >>> window >>> >> >> > size >>> >> >> > for each pixel, but I don't see in the code anything related to >>> that >>> >> >> > >>> >> >> > Maybe is this paper related to the TODOs in the code ? >>> >> >> > >>> >> >> > Was it planned to implement such a variable window approach ? >>> >> >> > >>> >> >> > Or if it is already implemented, could you point me where ? >>> >> >> > >>> >> >> > Thank you >>> >> >> > >>> >> >> > rawfiner >>> >> >> > >>> >> >> > >>> >> >> > >>> >> >> > >>> >> >> > 2018-01-26 9:05 GMT+01:00 johannes hanika <hana...@gmail.com>: >>> >> >> >> >>> >> >> >> hi, >>> >> >> >> >>> >> >> >> if you want, absolutely do play around with K. in my tests it >>> did >>> >> >> >> not >>> >> >> >> lead to any better denoising. to my surprise a larger K often >>> led to >>> >> >> >> worse results (for some reason often the relevance of discovered >>> >> >> >> patches decreases with distance from the current point). that's >>> why >>> >> >> >> K >>> >> >> >> is not exposed in the gui, no need for another irrelevant and >>> >> >> >> cryptic >>> >> >> >> parameter. if you find a compelling case where this indeed >>> leads to >>> >> >> >> better denoising we could rethink that. >>> >> >> >> >>> >> >> >> in general NLM is a 0-th order denoising scheme, meaning the >>> prior >>> >> >> >> is >>> >> >> >> piecewise constant (you claim the pixels you find are trying to >>> >> >> >> express /the same/ mean, so you average them). if you let that >>> >> >> >> algorithm do what it would really like to, it'll create >>> unpleasant >>> >> >> >> blotches of constant areas. so for best results we need to tone >>> it >>> >> >> >> down one way or another. >>> >> >> >> >>> >> >> >> cheers, >>> >> >> >> jo >>> >> >> >> >>> >> >> >> >>> >> >> >> >>> >> >> >> On Fri, Jan 26, 2018 at 7:36 AM, rawfiner <rawfi...@gmail.com> >>> >> >> >> wrote: >>> >> >> >> > Hi >>> >> >> >> > >>> >> >> >> > I am surprised to see that we cannot control the neighborhood >>> >> >> >> > parameter >>> >> >> >> > for >>> >> >> >> > the NLM algorithm (neither for the denoise non local mean, >>> nor for >>> >> >> >> > the >>> >> >> >> > denoise profiled) from the GUI. >>> >> >> >> > I see in the code (denoiseprofile.c) this TODO that I don't >>> >> >> >> > understand: >>> >> >> >> > "// >>> >> >> >> > TODO: fixed K to use adaptive size trading variance and bias!" >>> >> >> >> > And just some lines after that: "// TODO: adaptive K tests >>> here!" >>> >> >> >> > (K is the neighborhood parameter of the NLM algorithm). >>> >> >> >> > >>> >> >> >> > In practice, I think that being able to change the >>> neighborhood >>> >> >> >> > parameter >>> >> >> >> > allows to have a better noise reduction for one image. >>> >> >> >> > For example, choosing a bigger K allows to reduce the spotted >>> >> >> >> > aspect >>> >> >> >> > that >>> >> >> >> > one can get on high ISO images. >>> >> >> >> > >>> >> >> >> > Of course, increasing K increase computational time, but I >>> think >>> >> >> >> > we >>> >> >> >> > could >>> >> >> >> > find an acceptable range that would still be useful. >>> >> >> >> > >>> >> >> >> > >>> >> >> >> > Is there any reason for not letting the user control the >>> >> >> >> > neighborhood >>> >> >> >> > parameter in the GUI ? >>> >> >> >> > Also, do you understand the TODOs ? >>> >> >> >> > I feel that we would probably get better denoising by fixing >>> >> >> >> > these, >>> >> >> >> > but >>> >> >> >> > I >>> >> >> >> > don't understand them. >>> >> >> >> > >>> >> >> >> > I can spend some time on these TODOs, or to add the K >>> parameter to >>> >> >> >> > the >>> >> >> >> > interface if you think it is worth it (I think so but it is >>> only >>> >> >> >> > my >>> >> >> >> > personal >>> >> >> >> > opinion), but I have to understand what the TODOs mean before >>> >> >> >> > >>> >> >> >> > Thank you for your help >>> >> >> >> > >>> >> >> >> > rawfiner >>> >> >> >> > >>> >> >> >> > >>> >> >> >> > >>> >> >> >> > >>> >> >> >> > ____________________________________________________________ >>> _______________ >>> >> >> >> > darktable developer mailing list to unsubscribe send a mail to >>> >> >> >> > darktable-dev+unsubscr...@lists.darktable.org >>> >> >> >> >>> >> >> >> >>> >> >> >> >>> >> >> >> ____________________________________________________________ >>> _______________ >>> >> >> >> darktable developer mailing list >>> >> >> >> to unsubscribe send a mail to >>> >> >> >> darktable-dev+unsubscr...@lists.darktable.org >>> >> >> >> >>> >> >> > >>> >> >> >>> >> >> >>> >> >> ____________________________________________________________ >>> _______________ >>> >> >> darktable developer mailing list >>> >> >> to unsubscribe send a mail to >>> >> >> darktable-dev+unsubscr...@lists.darktable.org >>> >> >> >>> >> > >>> > >>> > >>> > ____________________________________________________________ >>> _______________ >>> > darktable developer mailing list to unsubscribe send a mail to >>> > darktable-dev+unsubscr...@lists.darktable.org >>> > >>> > >>> > >>> > ____________________________________________________________ >>> _______________ >>> > darktable developer mailing list to unsubscribe send a mail to >>> > darktable-dev+unsubscr...@lists.darktable.org >>> ____________________________________________________________ >>> _______________ >>> darktable developer mailing list >>> to unsubscribe send a mail to darktable-dev+unsubscribe@list >>> s.darktable.org >>> >>> >> ___________________________________________________________________________ >> darktable developer mailing list to unsubscribe send a mail to >> darktable-dev+unsubscr...@lists.darktable.org >> >> >> >> ___________________________________________________________________________ >> darktable developer mailing list to unsubscribe send a mail to >> darktable-dev+unsubscr...@lists.darktable.org >> > > ___________________________________________________________________________ darktable developer mailing list to unsubscribe send a mail to darktable-dev+unsubscr...@lists.darktable.org
